Multimedia Concept Database Enrichment via Homogenous Signatures
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Solution Overview
Problem
Current multimedia content management systems face challenges in efficiently organizing and searching large volumes of multimedia data due to the abstract and complex nature of multimedia content, which is not adequately represented by existing metadata, leading to inefficiencies in data retrieval and scalability issues.
Innovation Solution
The method involves generating signatures for multimedia content elements, matching them with existing concepts in a database, creating a reduced representation, and generating new homogenous concepts based on top matching elements, which are then added to the database to enrich the concept representation, allowing for more efficient and accurate content management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing metadata solutions are used to describe multimedia content, then the content can be textually represented, but the representation is abstract and complex and not adequately defined
Solution Approach 1:
The patent transforms multimedia content from abstract metadata representations into concrete visual feature representations. By extracting actual visual parameters (edges, textures, colors, shapes) directly from the content rather than relying on textual metadata, the system changes the representation parameters from abstract strings to measurable visual features, thereby improving representation accuracy while reducing complexity.
Solution Approach 2:
The patent creates visual feature representations that are direct copies or derivatives of the actual multimedia content characteristics. Instead of using separate metadata that may not accurately reflect the content, the system generates feature representations by directly analyzing and copying visual properties from the content itself, ensuring the representation faithfully mirrors the actual content features.
2Reliability
If model-based methods are used to define multimedia content, then content can be described, but the structure is too abstract and complex to be adequately represented
Solution Approach 1:
The patent segments multimedia content into distinct visual feature components such as edges, textures, colors, and shapes. By dividing the complex content into these manageable segments and representing each with specific visual parameters, the system reduces the overall complexity while maintaining reliable representation of each content aspect through dedicated feature extractors.
Solution Approach 2:
The patent changes the representation parameters from abstract model-based descriptions to concrete visual measurements. By using measurable parameters like edge orientation, texture frequency, color histograms, and shape descriptors, the system achieves more reliable content description that directly corresponds to observable content properties rather than abstract interpretations.
3Measurement precision
If large volumes of multimedia data are stored with detailed representations, then content can be accurately represented, but processing efficiency decreases and scalability issues arise
Solution Approach 1:
The patent extracts only the essential visual features from multimedia content, taking out the most discriminative parameters (edges, textures, colors, shapes) while discarding redundant information. This selective extraction maintains measurement precision for content identification while significantly reducing the data volume that needs to be stored and processed, thereby improving processing efficiency and scalability.
Solution Approach 2:
The patent performs preliminary feature extraction and content analysis before storage, pre-processing the multimedia data into compact visual feature representations. By conducting this extraction action in advance rather than during retrieval or processing, the system maintains high measurement precision for content matching while improving productivity during actual search and processing operations.
4Loss of information
If redundant data is retained in the database, then comprehensive content coverage is maintained, but processing time increases and efficiency decreases
Solution Approach 1:
The patent extracts and retains only the essential visual feature parameters needed for content identification and matching, removing redundant data that does not contribute to content discrimination. By keeping only the most informative features (edge patterns, texture characteristics, color distributions, shape descriptors), the system maintains complete content coverage for accurate matching while reducing processing time through smaller data volumes.
Data Source
AI summary
A system and method for enriching a concept database with homogenous concepts. The method includes determining, based on signatures of a first multimedia content element (MMCE) and signatures of a plurality of existing concepts in the concept database, at least one first concept; generating a reduced representation of the first MMCE, wherein the reduced representation excludes the signatures of the first MMCE that match the at least one first concept; comparing the reduced representation to signatures representing a plurality of second MMCEs to select a first plurality of top matching second MMCEs; generating, based on the reduced representation and the first plurality of top matching second MMCEs, at least one second concept; determining, for each second concept, whether the second concept is a homogenous concept, wherein each homogenous concept uniquely represents the same content; and adding each homogenous concept to the concept database.